A fundamental challenge for many mobile computing applications is data management. Data should be available anywhere at anytime. However, this task is particularly difficult to support with respect to managing a user's personal data (e.g., data files, email, and cached web pages) across a collection of personal devices (e.g., a laptop, PDA, and mobile phone). Two properties of the mobile environment make data management particularly challenging: network disconnection and device resource constraints, particularly energy constraints. The goal of this work is to provide maximal availability and consistency of personal data as well as maximal aggregate device lifetime for a collection of personal devices. To accomplish this goal, the project develops a set of cooperative prefetching algorithms that enable devices to retrieve relevant data when connected to the information source. Further, the algorithms efficiently distribute the energy burden of performing prefetching across the collection of devices. The evaluation investigates the performance of the algorithms in an implementation running on a collection of laptop computers and iPAQs. Two primary metrics are considered: the aggregate lifetime of the devices and the hit rate of user requests for data. The overarching impacts of this work will be improved usability of mobile devices and applications and maximization of mobile device lifetime. Further, the student participants in this project are undergraduate women from Mount Holyoke College, one of the nation's finest all-women undergraduate institutions. Therefore, the project will also serve to improve the overall participation of women in systems research.
